# Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series

arXiv: https://arxiv.org/abs/2310.15555 · HTML: https://mlsanity.com/paper/2310.15555/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 87 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Adversarially robust transfer learning](https://mlsanity.com/paper/1905.08232/) (2019)
- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Transfer Learning for Estimating Causal Effects using Neural Networks](https://mlsanity.com/paper/1808.07804/) (2018)
- [Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources](https://mlsanity.com/paper/2305.10559/) (2023)
- [A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers](https://mlsanity.com/paper/2302.12168/) (2023)
- [Transfer Learning for Electricity Price Forecasting](https://mlsanity.com/paper/2007.03762/) (2020)
- [National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?](https://mlsanity.com/paper/2107.06174/) (2021)
- [In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance](https://mlsanity.com/paper/2302.13046/) (2023)
- [Targeted demand response for flexible energy communities using clustering techniques](https://mlsanity.com/paper/2303.00186/) (2023)
- [DeepTSF: Codeless machine learning operations for time series forecasting](https://mlsanity.com/paper/2308.00709/) (2023)

## Built on by (most cited first, 4 of 4)

- [Transfer Learning on Transformers for Building Energy Consumption Forecasting -- A Comparative Study](https://mlsanity.com/paper/2410.14107/) (2024)
- [Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework](https://mlsanity.com/paper/2412.07782/) (2024)
- [Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning](https://mlsanity.com/paper/2502.16896/) (2025)
- [A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support](https://mlsanity.com/paper/2504.06055/) (2025)

## Abstract (arXiv)

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.